Edge Inference Engine for Deep & Random Sparse Neural Networks with 4-bit Cartesian-Product MAC Array and Pipelined Activation Aligner

Abstract

A 4b-quantized convolutional neural network (CNN) inference engine for edge-AI is presented featuring a Cartesian-product MAC array and pipelined activation aligners targeting deep-/random-pruned models. A 40nm prototype with 32x32 MACs and 5Mb SRAM runs at 534 MHz, 1.07 TOPS, 352 mW at 1.1V, and attains 5.30 dense TOPS/W, 234 MHz at 0.8V. Sparse TOPS/W reaches 26.5 when running a randomly pruned model (after 88% pruning). Training algorithms for obtaining highly efficient sparse/quantized models are also proposed.